Lessons from America: The role of business improvement districts as an agent of urban regeneration
Bibliographic record
Abstract
The government intends to bring out new legislation in 2004 to enable cities to set up Business Improvement Districts (BIDs). These were introduced in Canada in the 1970s but have been most commonly adopted in the USA during the 1980s and 1990s. There are wide variants in terms of scale, budget, role, power and mission, and so BIDs have the advantage of being easily tailored to fit local conditions. In essence they represent a voluntary tax that local businesses impose on themselves, administer themselves and spend themselves. The money is typically spent on combating crime, providing a clean, attractive environment and promoting the local economy of the neighbourhood. BIDs are primarily though not exclusively found in retail areas where businesses have a clear interest in improving the appearance and safety of an area. This paper highlights what can be learned from the American experience of BIDs in terms of scale, scope, strengths, weaknesses and lessons for the implementation of BIDs in the UK. The paper uses secondary research and is the result both of findings derived from American analysis of BIDs and from detailed reading of the websites of a cross section of BIDs across the USA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".